Correcting the impact of docking pose generation error on binding affinity prediction

Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets

(2015) Hongjian Li et al.   Molecular Informatics

Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest

(2015) Hongjian Li et al.   MOLECULES

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

(2015) Qurrat Ul Ain et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study

(2014) Hongjian Li et al.   BMC BIOINFORMATICS

iview: an interactive WebGL visualizer for protein-ligand complex

(2014) Hongjian Li et al.   BMC BIOINFORMATICS

Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

(2014) Pedro J. Ballester et al.   Journal of Chemical Information and Modeling

istar: A Web Platform for Large-Scale Protein-Ligand Docking

(2014) Hongjian Li et al.   PLoS One

SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes

(2013) David Zilian et al.   Journal of Chemical Information and Modeling

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

(2012) P. J. Ballester et al.   Journal of the Royal Society Interface

Robust Scoring Functions for Protein–Ligand Interactions with Quantum Chemical Charge Models

(2011) Jui-Chih Wang et al.   Journal of Chemical Information and Modeling

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

(2010) Pedro J. Ballester et al.   BIOINFORMATICS

Comparative Assessment of Scoring Functions on a Diverse Test Set

(2009) Tiejun Cheng et al.   Journal of Chemical Information and Modeling